.png){kind=icon}
Return
Interpretable Sequence Classification via Decision Set
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2026.3665720.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sequence classification is a fundamental research issue in data mining and machine learning. However, existing sequence classification methods primarily focus on improving the prediction performance. Although a few methods attempt to enhance the interpretability, they often fail to provide intuitive model explanations and come with high computational costs. To fill this gap, we propose an interpretable sequence classification algorithm based on decision set. Each rule in the decision set is only associated with one discriminative pattern (subsequence) and the classification decision is made based on one best-matched rule. Hence, the proposed method has good interpretablility since the classification decision is solely determined by one simple intuitive rule. Experimental results on real-world data sets demonstrate that our algorithm outperforms the state-of-the-art interpretable sequence classification methods in terms of both interpretability and classification accuracy.
Keywords:
Sequence classification
decision set
interpretable classification
sequential pattern mining
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
Organization
View more organizations
Cited Papers
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
OAAI
OAAIShareSaveShareSaveInterpretable machine learning: Fundamental principles and 10 grand challenges.png)
STATISTICS SURVEYSOAAI
STATISTICS SURVEYSIF15.4
OAAIShareSaveInterpretable and Steerable Sequence Learning via PrototypesProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
OAAI
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIF0
OAAIShareSaveExplainable AI for Time Series Classification: A Review, Taxonomy and Research DirectionsIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSaveShareSave View more